https://github.com/NevSNev/UniST
공식 코드Two Birds, One Stone: A Unified Framework for Joint Learning of Image and Video Style Transfers
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Two Birds, One Stone: A Unified Framework for Joint Learning of Image and Video Style Transfers
PixelShuffler: A Simple Image Translation Through Pixel Rearrangement
Interactive Video Stylization Using Few-Shot Patch-Based Training
Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfer
Parameter-Free Style Projection for Arbitrary Style Transfer
Exploring Contextual Word-level Style Relevance for Unsupervised Style Transfer
UniVST: A Unified Framework for Training-free Localized Video Style Transfer
TextBox 2.0: A Text Generation Library with Pre-trained Language Models
GenerSpeech: Towards Style Transfer for Generalizable Out-Of-Domain Text-to-Speech
Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation
Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement Approach
Improving Inference-Time Optimisation for Vocal Effects Style Transfer with a Gaussian Prior
GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework
Controllable Artistic Text Style Transfer via Shape-Matching GAN
MOVE: Effective and Harmless Ownership Verification via Embedded External Features
Sandwich Batch Normalization: A Drop-In Replacement for Feature Distribution Heterogeneity
Lipstick ain't enough: Beyond Color Matching for In-the-Wild Makeup Transfer
Image-to-Image Translation via Group-wise Deep Whitening-and-Coloring Transformation
Decoupling Global and Local Representations via Invertible Generative Flows
Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer
MuseMorphose: Full-Song and Fine-Grained Piano Music Style Transfer with One Transformer VAE
SinIR: Efficient General Image Manipulation with Single Image Reconstruction
Structured Content Preservation for Unsupervised Text Style Transfer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.